Automated Healthcare Record Discovery via Network Graph Mapping
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Solution Overview
Problem
Organizations face challenges in managing and tracing personal healthcare information (PHI) due to manual discovery processes leading to incomplete inventories and loss of data origins, especially in decentralized electronic health record systems, where data is copied, transferred, or stored across IT assets, making it difficult to demonstrate compliance and maintain accurate provenance.
Innovation Solution
A computer program product and method that uses a network scan based on a seed set to map connected nodes storing archived healthcare records, access and ingest content, and determine longitudinal healthcare records, thereby organizing and tracing PHI data through a graph-based system, preserving provenance information and documenting data lineage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual discovery processes are used to inventory healthcare records, then data origins can be traced, but the inventory is incomplete and time-consuming
Solution Approach 1:
The patent replaces manual mechanical discovery processes with automated electronic network scanning and graph-based data structure construction. The system automatically discovers connected nodes storing healthcare records by scanning network connections, building a graph representation of data locations, and tracing data lineage without human intervention, thereby achieving complete inventory while eliminating time loss.
2Ease of operation
If data is copied and transferred across decentralized IT assets, then data accessibility is improved, but provenance information is lost
Solution Approach 1:
The patent adds a new dimensional layer by constructing a graph-based data structure that overlays the decentralized physical storage system. This graph dimension tracks node connections, data locations, and lineage relationships, enabling provenance tracking alongside data accessibility without interfering with the operational benefits of decentralized storage.
Solution Approach 2:
The patent introduces a graph-based data structure as an intermediary between the decentralized storage system and the user. This intermediary layer maintains provenance information by tracking node connections and data lineage, allowing users to access data across distributed assets while the graph structure preserves the complete trail of data origins and transformations.
3Quantity of substance
If automated network scanning is performed to discover all connected nodes, then complete inventory is achieved, but system complexity increases
Solution Approach 1:
The patent segments the discovery process into distinct automated components: network scanning module that identifies connected nodes, graph construction module that builds the data structure, and lineage tracking module that records provenance. This segmentation allows the system to achieve complete node discovery while managing complexity through modular, automated processes rather than monolithic complex systems.
Data Source
AI summary
Described herein are methods, systems, apparatuses and products for automated information discovery and traceability for evidence generation. An aspect provides for accessing a mapping of a plurality of connected nodes stored in a memory device, said mapping being discovered via a network scan based on a seed set, said plurality of connected nodes storing a plurality of archived healthcare records; accessing content stored in a memory device and ingested from said plurality of connected nodes; and determining a longitudinal healthcare record from the mapping and the content ingested from said plurality of connected nodes. Other embodiments are disclosed.